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Related Questions
- How does data augmentation impact the performance of pre-trained language models when fine-tuning on low-resource language datasets?
- Can you explain the concept of 'data augmentation' in the context of natural language processing and how it applies to LLMs?
- What are some common techniques used for data augmentation in language models, and how do they improve generalizability?
- Can data augmentation help mitigate the effect of domain bias in LLMs?
- How does data augmentation compare to other techniques such as transfer learning and multi-task learning in improving generalizability?
- What are the potential drawbacks or limitations of using data augmentation for improving LLM generalizability?
- Can data augmentation be used to improve the robustness of LLMs to out-of-distribution data or adversarial attacks?
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